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Sathiaseelan, J.G.R.
- Texture Feature Extraction with Medical Image using Glcm and Machine Learning Techniques
Abstract Views :189 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, IN
1 Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2735-2740Abstract
Bones are a vital component of the human body. Bone provides the capacity to move the body. Humans have a high rate of bone fractures. The X-ray image is used by the doctors to identify the fractured bone. The manual fracture identification technique takes a long time and has a high risk of mistake. Machine learning and artificial intelligence are critical in resolving difficult difficulties in clinical imaging. Both medical practitioners and patients benefit from machine learning and artificial intelligence. Nowadays, an automatic system is built to detect abnormalities in bone X-ray pictures with great accuracy. To achieve high accuracy with limited resources, image pre-processing methods are employed to improve the quality of medical images. Image pre-processing entails steps such as noise removal and contrast enhancement, resulting in an instantaneous abnormality detection system. In image classification challenges, the Gray Level Co-occurrence Matrix (GLCM) texture features are commonly utilised. The second order statistical information about grey levels between nearby pixels in an image is represented by GLCM. In this work, we used various machine learning algorithms to categorise the MURA (musculoskeletal radiographs) dataset’s bone X-ray images into fractures and no fracture categories. For anomaly detection, the four different classifiers SVM (support vector machine), Random Forest, Logistic Regression, and Decision tree are utilised. The aforementioned abnormality detection in X-ray pictures is evaluated using five statistical criteria, including Sensitivity, Specificity, Precision, Accuracy, and F1 Score, all of which indicate considerable improvement.Keywords
Machine Learning, GLCM, SVM, Random Forest, Logistic Regression, Decision Tree, MURA, Bone Fractures
References
- W. Abbas, Syed M. Adnan, M. Arshad Javid, Fahad Majeed, Talha Ahsan and Syed Saqlian Hassan, “Lower Leg Bone Fracture Detection and Classification Using Faster RCNN for X-Rays Images”, Proceedings of IEEE International Conference on Multitopic, pp. 1-6, 2020.
- Y. Ma and Yixin Luo, “Bone Fracture Detection Through the Two-Stage System of Crack-Sensitive Convolutional Neural Network”, Informatics in Medicine Unlocked, Vol. 22, pp. 1-13, 2021.
- Firat Hardalac, Boran Demirciler and Fatih Mert, “Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models”, Proceedings of IEEE International Conference on Machine and Deep Learning, pp. 1-13, 2021.
- Naveed Iqbal, Rafia Mumtaz, Uferah Shafi and Syed Mohammad Hassan Zaidi, “Gray Level Co-Occurrence Matrix (GLCM) Texture-Based Crop Classification using Low Altitude Remote Sensing Platforms”, Computer Science, Vol. 7, pp. 1-13, 2021.
- Rundo, Leonardo, Andrea Tangherloni, Paolo Cazzaniga, Matteo Mistri, Simone Galimberti, Ramona Woitek, Evis Sala, Giancarlo Mauri and Marco S. Nobile, “A CUDA-Powered Method for the Feature Extraction and Unsupervised Analysis of Medical Images”, The Journal of Supercomputing, Vol. 89, pp. 1-18, 2021.
- Mireille Pouyap, Laurent Bitjoka, Etienne Mfoumou and Denis Toko, “Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-Naïve Bayes Classification”, Journal of Signal and Information Processing, Vol. 12, No. 4, pp. 71-85, 2021.
- Dian Li, Cheng Wu and Yiming Wang, “A Novel Iris Texture Extraction Scheme for Iris Presentation Attack Detection”, Journal of Image and Graphics, Vol. 9, No. 3, pp.1-12, 2021.
- K. Padmavathi and Maya V. Karki. “Texture Feature Extraction and Classification of Brain Neoplasm in MR Images using Machine Learning Techniques”, International Journal of Recent Technology and Engineering, Vol. 8, No. 5, pp. 1-9, 2020.
- Stefania Barburiceanu, Romulus Terebes and Serban Meza, “3D Texture Feature Extraction and Classification using GLCM and LBP-Based Descriptors”, Applied Sciences, Vol. 11, No. 5, pp. 2332-2339, 2021.
- Dharmender Kumar, “Feature Extraction and Selection of Kidney Ultrasound Images using GLCM and PCA”, Procedia Computer Science, Vol. 167, pp. 1722-1731, 2020.
- Keon Myung Lee, Sang Yeon Lee, Chan Sik Han and Seung Myung Choi, “Long Bone Fracture Type Classification for Limited Number of CT Data with Deep Learning”, Proceedings of Annual ACM Symposium on Applied Computing, pp. 1090-1095. 2020.
- Guillaume Reichert, Ali Bellamine, Matthieu Fontaine, Beatrice Naipeanu, Adrien Altar, Elodie Mejean, Nicolas Javaud and Nathalie Siauve, “How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?”, Journal of Imaging, Vol. 7, No. 7, pp. 105-113, 2021.
- Adrien Muller, Nikos Karathanasopoulos, Christian C. Roth and Dirk Mohr, “Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments”, International Journal of Mechanical Sciences, Vol. 209, pp. 1-15, 2021.
- D.P. Yadav, Ashish Sharma, Madhusudan Singh and Ayush Goyal, “Feature Extraction-based Machine Learning for Human Burn Diagnosis from Burn Images”, IEEE Journal of Translational Engineering in Health and Medicine, Vol. 7, pp. 1-7, 2019.
- Sandeep Rathor and R.S. Jadon, “The Art of Domain Classification and Recognition for Text Conversation using Support Vector Classifier”, International Journal of Arts and Technology, Vol. 11, No. 3, pp. 309-324, 2019.
- Prediction of Seed Purity and Variety Identification Using Image Mining Techniques
Abstract Views :110 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, IN
1 Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2715-2722Abstract
Seed is a little embryonic plant that can be used to introduce plant infections to new areas while also allowing them to survive from one cropping season to the next. Seed health is a well-known component in modern agricultural science for achieving the required plant population and yield. Seed-borne fungus are a major biotic constraint in seed production around the world. The detection of seed-borne pathogens by seed health testing is a crucial step in the treatment of crop diseases. Speed and accuracy are critical requirements for long-term economic growth, competitiveness, and sustainability in agricultural output. Because human judgements in identifying objects and situations are variable, subjective, and delayed, seed prediction activities are costly and unreliable. Machine vision technology provides a nondestructive, cost-effective, quick, and accurate option for automated procedures. Seed variety, seed type (country seed or hybrid seed), seed health, and purity prediction were the four basic processes. We began the first procedure by aligning the seed bodies in the same direction using a seed orientation approach. Then, to detect atypical physical seed samples, a quality screening procedure was used. Their physical characteristics, such as shape, colour, and texture, were retrieved to serve as data representations for the prediction. This research introduces a new fuzzy cognitive map (FCM) model based on deep learning neural networks that predicts seed purity tests using data from biological investigations. The relevant data features from the seed test are extracted by FCM, which then effectively initialises the deep neural networks. The Levenberg–Marquardt (LM) technique for deep neural networks was discovered to improve seed purity test prediction. Four statistical machine learning algorithms (BP-ANN, Multivariate regression, and FCMLM deep learning). Furthermore, we demonstrated an improvement in the system's overall performance in terms of data quality, including seed orientation and quality screening. In independent numerical testing, the correlation coefficient between predicted values and true values acquired from experiments reached 0.9.Keywords
Fuzzy Cognitive Map, Deep Learning, FCMLM Deep Learning, BP-ANN, Multivariate Regression, Seed PurityReferences
- B.S. Anami, Naveen N. Malvade and Surendra Palaiah, “Automated Recognition and Classification of Adulteration Levels from Bulk Paddy Grain Samples”, Information Processing in Agriculture, Vol. 6, No. 1, pp. 47-60, 2019.
- W. Liu and Feifei Chen, “Rice Seed Purity Identification Technology using Hyperspectral Image with LASSO Logistic Regression Model”, Sensors, Vol. 21, No. 13, pp. 1-14, 2021.
- Nadia Ansari, Sharmin Sultana Ratri, Afroz Jahan, Muhammad Ashik-E-Rabbani and Anisur Rahman, “Inspection of Paddy Seed Varietal Purity using Machine Vision and Multivariate Analysis”, Journal of Agriculture and Food Research, Vol. 3, pp. 1-12, 2021.
- Xiaolong Li, Zhenni He, Fei Liu and Rongqin Chen, “Fast Identification of Soybean Seed Varieties using Laser-Induced Breakdown Spectroscopy Combined with Convolutional Neural Network”, Frontiers in Plant Science, Vol. 12, No. 2, pp. 1-12, 2021.
- P. Lin, X.L. Li, Y.M. Chen and Y. He, “A Deep Convolutional Neural Network Architecture for Boosting
- Image Discrimination Accuracy of Rice Species”, Food and Bioprocess Technology, Vol. 11, No. 4, pp. 765-773, 2018.
- J. Chen, Xudong Gao, Jia Rong and Xiaoguang Gao, “The Dynamic Extensions of Fuzzy Grey Cognitive Maps”, IEEE Access, Vol. 9, pp. 98665-98678, 2021.
- Taha Mansouri, Ahad ZareRavasan and Amir Ashrafi, “A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance”, Journal of Information Technology Education: Research, Vol. 20, pp. 221-243, 2021.
- A. Ali, Samreen Naeem, Sidra Rafique, Farrukh Jamal, Christophe Chesneau and Sania Anam, “Machine Learning Approach for the Classification of Corn Seed using Hybrid Features”, International Journal of Food Properties, Vol. 23, No. 1, pp. 1110-1124, 2020.
- Iqbal H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions”, SN Computer Science, Vol. 2, No. 6, pp. 1-20, 2021.
- Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng and Jun Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 68, No. 3, pp. 1-13, 2021.
- Z. Luan and Yan Yang, “Sunflower Seed Sorting based on Convolutional Neural Network”, Proceedings of 11th International Conference on Graphics and Image Processing, pp.1-12, 2020.
- K. Tatsumi and X. Mengxue, “Prediction of Plant-Level Tomato Biomass and Yield using Machine Learning with Unmanned Aerial Vehicle Imagery”, Proceedings of International Conference on Graphics and Image Processing, pp. 1-8, 2021.
- S.J. Symons and R.G. Fulcher, “Determination of Wheat Kernel Morphological Variation by Digital Image Analysis: I. Variation in Eastern Canadian Milling Quality Wheats”, Journal of Cereal Science, Vol. 8, No. 3, pp. 211-218, 1988.
- Jared Taylor, Chien-Ping Chiou and Leonard J. Bond, “A Methodology for Sorting Haploid and Diploid Corn Seed using Terahertz Time Domain Spectroscopy and Machine Learning”, AIP Conference Proceedings, Vol. 2102, No. 1, pp. 1-6, 2019.